When 15-year-old Ahaan Rungta joined the freshman class at MIT this fall, he had already been studying at the storied institution for a decade.

By himself. At his home in Fort Lauderdale, Florida.

Rungta, who was being homeschooled, was introduced to OpenCourseWare (OCW) by his mother when he was 5. OCW is a MIT's online learning platform that makes all of the school's courses available for free. It allows anyone from across the globe to access courses and brag to friends that they're getting the best adult continuing education in the world.

Rungta didn't wait for the "adult" part, instead getting his entire elementary and high school education through the program.

"I studied math through OCW's Highlights for High School program, and when I was ready for Linear Algebra, I watched all of Professor Gil Strang's 18.06 video lectures. From the time I was 5, I learned exclusively from OCW. And I knew then I wanted to go to MIT," the prodigy told MIT News.

While not the only institution to open source its classwork, MIT offers one of the most comprehensive class lists. And its in good company. Yale, Carnegie Mellon and Harvard Medical School, among others, offer similar programs.

The emergence of free online learning is not just changing lives for child prodigies. It has democratized higher education, giving anyone with Internet access an opportunity to learn from some of the most elite teachers in the world.

Some consider it revolutionary.

"Nothing has more potential to lift more people out of poverty - by providing them an affordable education to get a job or improve in the job they have. Nothing has more potential to unlock a billion more brains to solve the world's biggest problems," wrote Thomas Friedman in the New York Times.

From the very first time he used OCW, Rungta knew he wanted to attend college at MIT. Now that his dream has come true, the biggest question is: what will he major in?

"In an ideal world, I would want to major in everything," the teenager told MIT News.

It's easy to mistake this photo for some kind of surreal landscape painting, but this image in fact shows off the imagination of Google's advanced image detection software.
Similar to an artist with a blank canvas, Google's software constructed this image out of nothing, or essentially nothing, anyway. This photo began as random noise before software engineers coaxed this pattern out of their machines.
How is it possible for software to demonstrate what appears to be an artistic sensibility? It all begins with what is basically an artificial brain.

Artificial neural networks are systems consisting of between 10 and 30 stacked layers of synthetic neurons. In order to train the network, "each image is fed into the input layer, which then talks to the next layer, until eventually the 'output' layer is reached,"

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The layers work together to identify an image. The first layer detects the most basic information, such as the outline of the image. The next layers hone in on details about the shapes. The final output layer provides the "answer," or identification of the subject of an image.
Shown is Google's image software before and after processing an image of two ibis grazing to detect their outlines.

Searching for shapes in clouds isn't just a human pastime anymore.
Google engineers trained the software to identify patterns by feeding millions of images to the artificial neural network. Give the software constraints, and it will scout out patterns to recognize objects even in photos where the search targets are not present.
In this photo, for example, Google's software, like a daydreamer staring at the clouds, finds all kinds of different animals in the sky. This pattern emerged because the neural network was trained primarily on images of animals.

How the machine is trained will determine its bias in terms of recognizing certain objects within an otherwise unfamiliar image.
In this photo, a horizon becomes a pagoda; a tree is morphed into building; and a leaf is identified as a bird after image processing.
The objects may have similar outlines to their counterparts, but all of the entries in the "before" images aren't a part of the software's image vocabulary, so the system improvises.

When the software acknowledges an object, it modifies a photo to exaggerate the presence of that known pattern. Even if the software is able to correctly recognize the animals it has been trained to spot, image detection may be a little overzealous in identifying familiar shapes, particularly after the engineers send the photo back, telling the software to find more of the same, and thereby creating a feedback loop.
In this photo of a knight, the software appears to recognize the horse, but also renders the faces of other animals on the knight's helmet, globe and saddle, among other places.

Taken a step further, using the same image over several cycles in which the output is fed through over and over again, the artificial neural network will restructure an image into the shapes and patterns it has been trained to recognize.
Again borrowing from an image library heavy on animals, this landscape scene is transformed into a psychedelic dream scene where clouds are apparently made of dogs.

At its most extreme, the neural network can transform an image that started as random noise into a recognizable but still somewhat abstract kaleidoscopic expression of objects with which the software is most familiar.
Here, the software has detected a seemingly limitless number of arches in what was a random collection of pixels with no coherence whatsoever.

This landscape was created with a series of buildings.
Google is developing this technology in order to boost its image recognition software. Future photo services might recognize an object, a location or a face in a photo.
The engineers also suggest that the software could one day be a tool for artists that unlocks a new form of creative expression and may even shed light on the creative process more broadly.